1,721,095 research outputs found
Consideration-set heuristics
No full textConsumers often choose products by first forming a consideration set and then choosing from among considered products. When there are many products to screen (or many features to evaluate), it is rational for consumers to use consider-then-choose decision processes and to do so with heuristic decision rules. Managerial decisions (product development, marketing communications, etc.) depend upon the ability to identify and react to consumers' heuristic consideration-set rules. We provide managerial examples and review the state-of-the-art in the theory and measurement of consumers' heuristic consideration-set rules. Advances in greedoid methods, Bayesian inference, machine-learning, incentive alignment, measurement formats, and unstructured direct elicitation make it feasible and cost-effective to understand, quantify, and simulate “what-if” scenarios for a variety of heuristics. These methods now apply to a broad set of managerial problems including applications in complex product categories with large numbers of product features and feature-levels
Phenomena, theory, application, data, and methods all have impact
No full textIn his provocative essay on impactful research in this issue, my colleague and friend Gerry Tellis postulates that good papers are interesting and challenge common beliefs. He postulates further that such papers are based on ideas that are simple once proposed, although not always so obvious before being proposed. He recommends that impactful papers be focused and brief and begin with a study of the basic phenomena. Great advice
A marketing science perspective on recognition-based heuristics (and the fast-and-frugal paradigm)
Full text linkMarketing science seeks to prescribe better marketing strategies (advertising, product development, pricing, etc.). To do so we rely on models of consumer decisions grounded in empirical observations. Field experience suggests that recognition-based heuristics help consumers to choose which brands to consider and purchase in frequently-purchased categories, but other heuristics are more relevant in durable-goods categories. Screening with recognition is a rational screening rule when advertising is a signal of product quality, when observing other consumers makes it easy to learn decision rules, and when firms react to engineering-design constraints by offering brands such that a high-level on one product feature implies a low level on another product feature. Experience with applications and field experiments suggests four fruitful research topics: deciding how to decide (endogeneity), learning decision rules by self-reflection, risk reduction, and the difference between utility functions and decision rules. These challenges also pose methodological cautions.Sloan School of Managemen
Comment: New developments in product-line optimization
No full textProduct development is key to profitability. Without well-designed products that meet the needs of customers at a reasonable cost, the firm has no sales. And without sales, the firm has no profit. But designing profitable products is hard. Eppinger, Whitney, Smith, and Gebala (1994) estimate that for a moderately complex electro-mechanical product, close to a million decisions must be made before the product is brought to market. Many of these decisions are routine, but many are not. The two product-line-optimization papers in this journal address hard decisions
Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph
No full textWebsite morphing infers latent customer segments from clickstreams and then changes websites' look and feel to maximize revenue. The established algorithm infers latent segments from a preset number of clicks and then selects the best “morph” using expected Gittins indices. Switching costs, potential website exit, and all clicks prior to morphing are ignored. We model switching costs, potential website exit, and the (potentially differential) impact of all clicks to determine when to morph for each customer. Morphing earlier means more customer clicks are based on the optimal morph; morphing later reveals more about the customer's latent segment. We couple this within-customer optimization to between-customer expected Gittins index optimization to determine which website “look and feel” to give to each customer at each click. We evaluate the improved algorithm with synthetic data and with a proof-of-feasibility application to Japanese bank card loans. The proposed algorithm generalizes the established algorithm, is feasible in real time, performs substantially better when tuning parameters are identified from calibration data, and is reasonably robust to misspecification
Learning from Experience, Simply
Full text linkThere is substantial academic interest in modeling consumer experiential learning. However, (approximately) optimal solutions to forward-looking experiential learning problems are complex, limiting their behavioral plausibility and empirical feasibility. We propose that consumers use cognitively simple heuristic strategies. We explore one viable heuristic—index strategies—and demonstrate that they are intuitive, tractable, and plausible. Index strategies are much simpler for consumers to use but provide close-to-optimal utility. They also avoid exponential growth in computational complexity, enabling researchers to study learning models in more complex situations.
Well-defined index strategies depend on a structural property called indexability. We prove the indexability of a canonical forward-looking experiential learning model in which consumers learn brand quality while facing random utility shocks. Following an index strategy, consumers develop an index for each brand separately and choose the brand with the highest index. Using synthetic data, we demonstrate that an index strategy achieves nearly optimal utility at substantially lower computational costs. Using IRI data for diapers, we find that an index strategy performs as well as an approximately optimal solution and better than myopic learning. We extend the analysis to incorporate risk aversion, other cognitively simple heuristics, heterogeneous foresight, and an alternative specification of brands
Self-Reflection and Articulated Consumer Preferences
No full textAccurate measurement of consumer preferences reduces development costs and leads to successful products. Some product-development teams use quantitative methods such as conjoint analysis or structured methods such as Casemap. Other product-development teams rely on unstructured methods such as direct conversations with consumers, focus groups, or qualitative interviews. All methods assume that measured consumer preferences endure and are relevant for consumers' marketplace decisions. This article suggests that if consumers are not first given tasks to encourage preference self-reflection, unstructured methods may not measure accurate and enduring preferences. This paper provides evidence that consumers learn their preferences as they make realistic decisions. Sufficiently challenging decision tasks encourage preference self-reflection which, in turn, leads to more accurate and enduring measures. Evidence suggests further that if consumers are asked to articulate preferences before self-reflection, then that articulation interferes with consumers' abilities to articulate preferences even after they have a chance to self-reflect. The evidence that self-reflection enhances accuracy is based on experiments in the automotive and mobile phone markets. Consumers completed three rotated incentive-aligned preference measurement methods (revealed-preference measures [as in conjoint analysis], a structured method [Casemap], and an unstructured preference-articulation method). The stimuli were designed to be managerially relevant and realistic (53 aspects in automobiles, 22 aspects for mobile phones) so that consumers' decisions approximated in vivo decisions. One to three weeks later, consumers were asked which automobiles (or mobile phones) they would consider. Qualitative comments and response times are consistent with the implications of the measures of predictive ability
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